poverty_ken <- fread("poverty_data/poverty_ken.csv")
poverty_uga <- fread("poverty_data/poverty_uga.csv")
poverty_tza <- fread("poverty_data/poverty_tza.csv")
poverty_rwa <- fread("poverty_data/poverty_rwa.csv")
poverty_ea <- rbind(poverty_ken, poverty_uga, poverty_rwa, poverty_tza)
poverty_ea[sample(nrow(poverty_ea), 10)] %>% datatable(options = list(scrollX = TRUE))## [1] "Country Name" "Country ISO3" "Year" "Indicator Name"
## [5] "Indicator Code" "Value"## [1] "country_name" "country_iso3" "year" "indicator_name"
## [5] "indicator_code" "value"poverty_ea[, year := as.numeric(year)]
poverty_ea <- poverty_ea[!grepl("^Annu", indicator_name)]
poverty_ea_split <- split(poverty_ea, f = poverty_ea$indicator_name)
n <- length(poverty_ea_split)
my_plots <-htmltools::tagList()
for (i in 1:n) {
df = poverty_ea_split[[i]]
my_title = df[, unique(indicator_name)]
mn = df[, min(year)]
mx = df[, max(year)]
breaks = seq(mn, mx,by = 2)
p = ggplot(df, aes(year, value, group = country_name, color = country_name) ) +
geom_line()+
theme_fivethirtyeight()+
labs(title = my_title, x = "year", y = "%")+
scale_color_viridis_d(name="")+
scale_x_continuous(breaks = breaks)
my_plots[[i]] = ggplotly(p)
}
my_plotseconomy_and_growth <- fread("API_3_DS2_en_csv_v2_1743760.csv")
nms_old <- economy_and_growth[1,] %>% as.character()
nms_old## [1] "Country Name" "Country Code" "Indicator Name" "Indicator Code"
## [5] "1960" "1961" "1962" "1963"
## [9] "1964" "1965" "1966" "1967"
## [13] "1968" "1969" "1970" "1971"
## [17] "1972" "1973" "1974" "1975"
## [21] "1976" "1977" "1978" "1979"
## [25] "1980" "1981" "1982" "1983"
## [29] "1984" "1985" "1986" "1987"
## [33] "1988" "1989" "1990" "1991"
## [37] "1992" "1993" "1994" "1995"
## [41] "1996" "1997" "1998" "1999"
## [45] "2000" "2001" "2002" "2003"
## [49] "2004" "2005" "2006" "2007"
## [53] "2008" "2009" "2010" "2011"
## [57] "2012" "2013" "2014" "2015"
## [61] "2016" "2017" "2018" "2019"
## [65] "2020" "NA"economy_and_growth <- economy_and_growth[-1,]
names(economy_and_growth) <- nms_old
nms_new <- nms_old %>% tolower()
nms_new <- gsub("\\s", "_", nms_new)
nms_new## [1] "country_name" "country_code" "indicator_name" "indicator_code"
## [5] "1960" "1961" "1962" "1963"
## [9] "1964" "1965" "1966" "1967"
## [13] "1968" "1969" "1970" "1971"
## [17] "1972" "1973" "1974" "1975"
## [21] "1976" "1977" "1978" "1979"
## [25] "1980" "1981" "1982" "1983"
## [29] "1984" "1985" "1986" "1987"
## [33] "1988" "1989" "1990" "1991"
## [37] "1992" "1993" "1994" "1995"
## [41] "1996" "1997" "1998" "1999"
## [45] "2000" "2001" "2002" "2003"
## [49] "2004" "2005" "2006" "2007"
## [53] "2008" "2009" "2010" "2011"
## [57] "2012" "2013" "2014" "2015"
## [61] "2016" "2017" "2018" "2019"
## [65] "2020" "na"setnames(economy_and_growth, nms_old, nms_new)
id_vars <- c("country_name", "country_code",
"indicator_name", "indicator_code")
economy_and_growth <- melt(economy_and_growth,
id.vars = id_vars,
variable.factor = F,
value.factor = F,
variable.name = "year")
economy_and_growth[, value:= as.numeric(value)]
economy_and_growth[, year := as.numeric(year)]
economy_and_growth <- economy_and_growth[!is.na(value)|!is.na(year)]
economy_and_growth[sample(nrow(economy_and_growth), 10)] %>% datatable(options = list(scrollX = TRUE))gni_gdp_savings_vec <- c("Gross savings (% of GNI)",
"Gross savings (% of GDP)",
"Total debt service (% of exports of goods, services and primary income)",
"Total debt service (% of GNI)",
"Trade (% of GDP)",
"Current account balance (% of GDP)",
"Exports of goods and services (annual % growth)",
"Manufacturing, value added (annual % growth)",
"Price level ratio of PPP conversion factor (GDP) to market exchange rate")
ea_country <- c("Kenya", "Uganda", "Korea, Rep.", "Tanzania", "Singapore")
economy_and_growth[, unique(country_name)]## [1] "Aruba"
## [2] "Afghanistan"
## [3] "Angola"
## [4] "Albania"
## [5] "Andorra"
## [6] "Arab World"
## [7] "United Arab Emirates"
## [8] "Argentina"
## [9] "Armenia"
## [10] "American Samoa"
## [11] "Antigua and Barbuda"
## [12] "Australia"
## [13] "Austria"
## [14] "Azerbaijan"
## [15] "Burundi"
## [16] "Belgium"
## [17] "Benin"
## [18] "Burkina Faso"
## [19] "Bangladesh"
## [20] "Bulgaria"
## [21] "Bahrain"
## [22] "Bahamas, The"
## [23] "Bosnia and Herzegovina"
## [24] "Belarus"
## [25] "Belize"
## [26] "Bermuda"
## [27] "Bolivia"
## [28] "Brazil"
## [29] "Barbados"
## [30] "Brunei Darussalam"
## [31] "Bhutan"
## [32] "Botswana"
## [33] "Central African Republic"
## [34] "Canada"
## [35] "Central Europe and the Baltics"
## [36] "Switzerland"
## [37] "Channel Islands"
## [38] "Chile"
## [39] "China"
## [40] "Cote d'Ivoire"
## [41] "Cameroon"
## [42] "Congo, Dem. Rep."
## [43] "Congo, Rep."
## [44] "Colombia"
## [45] "Comoros"
## [46] "Cabo Verde"
## [47] "Costa Rica"
## [48] "Caribbean small states"
## [49] "Cuba"
## [50] "Curacao"
## [51] "Cayman Islands"
## [52] "Cyprus"
## [53] "Czech Republic"
## [54] "Germany"
## [55] "Djibouti"
## [56] "Dominica"
## [57] "Denmark"
## [58] "Dominican Republic"
## [59] "Algeria"
## [60] "East Asia & Pacific (excluding high income)"
## [61] "Early-demographic dividend"
## [62] "East Asia & Pacific"
## [63] "Europe & Central Asia (excluding high income)"
## [64] "Europe & Central Asia"
## [65] "Ecuador"
## [66] "Egypt, Arab Rep."
## [67] "Euro area"
## [68] "Eritrea"
## [69] "Spain"
## [70] "Estonia"
## [71] "Ethiopia"
## [72] "European Union"
## [73] "Fragile and conflict affected situations"
## [74] "Finland"
## [75] "Fiji"
## [76] "France"
## [77] "Faroe Islands"
## [78] "Micronesia, Fed. Sts."
## [79] "Gabon"
## [80] "United Kingdom"
## [81] "Georgia"
## [82] "Ghana"
## [83] "Gibraltar"
## [84] "Guinea"
## [85] "Gambia, The"
## [86] "Guinea-Bissau"
## [87] "Equatorial Guinea"
## [88] "Greece"
## [89] "Grenada"
## [90] "Greenland"
## [91] "Guatemala"
## [92] "Guam"
## [93] "Guyana"
## [94] "High income"
## [95] "Hong Kong SAR, China"
## [96] "Honduras"
## [97] "Heavily indebted poor countries (HIPC)"
## [98] "Croatia"
## [99] "Haiti"
## [100] "Hungary"
## [101] "IBRD only"
## [102] "IDA & IBRD total"
## [103] "IDA total"
## [104] "IDA blend"
## [105] "Indonesia"
## [106] "IDA only"
## [107] "Isle of Man"
## [108] "India"
## [109] "Not classified"
## [110] "Ireland"
## [111] "Iran, Islamic Rep."
## [112] "Iraq"
## [113] "Iceland"
## [114] "Israel"
## [115] "Italy"
## [116] "Jamaica"
## [117] "Jordan"
## [118] "Japan"
## [119] "Kazakhstan"
## [120] "Kenya"
## [121] "Kyrgyz Republic"
## [122] "Cambodia"
## [123] "Kiribati"
## [124] "St. Kitts and Nevis"
## [125] "Korea, Rep."
## [126] "Kuwait"
## [127] "Latin America & Caribbean (excluding high income)"
## [128] "Lao PDR"
## [129] "Lebanon"
## [130] "Liberia"
## [131] "Libya"
## [132] "St. Lucia"
## [133] "Latin America & Caribbean"
## [134] "Least developed countries: UN classification"
## [135] "Low income"
## [136] "Liechtenstein"
## [137] "Sri Lanka"
## [138] "Lower middle income"
## [139] "Low & middle income"
## [140] "Lesotho"
## [141] "Late-demographic dividend"
## [142] "Lithuania"
## [143] "Luxembourg"
## [144] "Latvia"
## [145] "Macao SAR, China"
## [146] "St. Martin (French part)"
## [147] "Morocco"
## [148] "Monaco"
## [149] "Moldova"
## [150] "Madagascar"
## [151] "Maldives"
## [152] "Middle East & North Africa"
## [153] "Mexico"
## [154] "Marshall Islands"
## [155] "Middle income"
## [156] "North Macedonia"
## [157] "Mali"
## [158] "Malta"
## [159] "Myanmar"
## [160] "Middle East & North Africa (excluding high income)"
## [161] "Montenegro"
## [162] "Mongolia"
## [163] "Northern Mariana Islands"
## [164] "Mozambique"
## [165] "Mauritania"
## [166] "Mauritius"
## [167] "Malawi"
## [168] "Malaysia"
## [169] "North America"
## [170] "Namibia"
## [171] "New Caledonia"
## [172] "Niger"
## [173] "Nigeria"
## [174] "Nicaragua"
## [175] "Netherlands"
## [176] "Norway"
## [177] "Nepal"
## [178] "Nauru"
## [179] "New Zealand"
## [180] "OECD members"
## [181] "Oman"
## [182] "Other small states"
## [183] "Pakistan"
## [184] "Panama"
## [185] "Peru"
## [186] "Philippines"
## [187] "Palau"
## [188] "Papua New Guinea"
## [189] "Poland"
## [190] "Pre-demographic dividend"
## [191] "Puerto Rico"
## [192] "Korea, Dem. Peopleâ\200\231s Rep."
## [193] "Portugal"
## [194] "Paraguay"
## [195] "West Bank and Gaza"
## [196] "Pacific island small states"
## [197] "Post-demographic dividend"
## [198] "French Polynesia"
## [199] "Qatar"
## [200] "Romania"
## [201] "Russian Federation"
## [202] "Rwanda"
## [203] "South Asia"
## [204] "Saudi Arabia"
## [205] "Sudan"
## [206] "Senegal"
## [207] "Singapore"
## [208] "Solomon Islands"
## [209] "Sierra Leone"
## [210] "El Salvador"
## [211] "San Marino"
## [212] "Somalia"
## [213] "Serbia"
## [214] "Sub-Saharan Africa (excluding high income)"
## [215] "South Sudan"
## [216] "Sub-Saharan Africa"
## [217] "Small states"
## [218] "Sao Tome and Principe"
## [219] "Suriname"
## [220] "Slovak Republic"
## [221] "Slovenia"
## [222] "Sweden"
## [223] "Eswatini"
## [224] "Sint Maarten (Dutch part)"
## [225] "Seychelles"
## [226] "Syrian Arab Republic"
## [227] "Turks and Caicos Islands"
## [228] "Chad"
## [229] "East Asia & Pacific (IDA & IBRD countries)"
## [230] "Europe & Central Asia (IDA & IBRD countries)"
## [231] "Togo"
## [232] "Thailand"
## [233] "Tajikistan"
## [234] "Turkmenistan"
## [235] "Latin America & the Caribbean (IDA & IBRD countries)"
## [236] "Timor-Leste"
## [237] "Middle East & North Africa (IDA & IBRD countries)"
## [238] "Tonga"
## [239] "South Asia (IDA & IBRD)"
## [240] "Sub-Saharan Africa (IDA & IBRD countries)"
## [241] "Trinidad and Tobago"
## [242] "Tunisia"
## [243] "Turkey"
## [244] "Tuvalu"
## [245] "Tanzania"
## [246] "Uganda"
## [247] "Ukraine"
## [248] "Upper middle income"
## [249] "Uruguay"
## [250] "United States"
## [251] "Uzbekistan"
## [252] "St. Vincent and the Grenadines"
## [253] "Venezuela, RB"
## [254] "British Virgin Islands"
## [255] "Virgin Islands (U.S.)"
## [256] "Vietnam"
## [257] "Vanuatu"
## [258] "World"
## [259] "Samoa"
## [260] "Kosovo"
## [261] "Yemen, Rep."
## [262] "South Africa"
## [263] "Zambia"
## [264] "Zimbabwe"gni_gdp_savings_df <- economy_and_growth[indicator_name %in% gni_gdp_savings_vec & country_name %in% ea_country ]gni_gdp_savings_df_split <- split(gni_gdp_savings_df, f = gni_gdp_savings_df$indicator_name)
n <- length(gni_gdp_savings_df_split)
my_plots_econ <-htmltools::tagList()
for (i in 1:n) {
df = gni_gdp_savings_df_split[[i]]
my_title = df[, unique(indicator_name)]
mn = df[, min(year)]
mx = df[, max(year)]
breaks = seq(mn, mx,by = 5)
p = ggplot(df, aes(year, value, group = country_name, color = country_name) ) +
geom_line(size = .3)+
theme_hc()+
labs(title = my_title, x = "year", y = "%")+
scale_color_colorblind(name="")+
scale_x_continuous(breaks = breaks)
my_plots_econ[[i]] = ggplotly(p)
}
my_plots_econ